






"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		logging.get_logger(__name__)  # pylint: disable=invalid-name







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[List, PIL.Image.Image, torch.Tensor]					) ->							Tuple:
  warnings.warn(
      'The preprocess method is deprecated and will be removed in a future version. Please'
      ' use VaeImageProcessor.preprocess instead'						,							_lowerCamelCase						,							)
  if isinstance(_lowerCamelCase						,							torch.Tensor					):
    return image
  elif isinstance(_lowerCamelCase						,							PIL.Image.Image					):
    lowerCamelCase_       	=		[image]

  if isinstance(image[0]						,							PIL.Image.Image					):
    lowerCamelCase_	,      lowerCamelCase_       	=		image[0].size
    lowerCamelCase_	,      lowerCamelCase_       	=		(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

    lowerCamelCase_       	=		[np.array(i.resize((w, h)						,							resample=PIL_INTERPOLATION['lanczos']					)					)[None, :] for i in image]
    lowerCamelCase_       	=		np.concatenate(_lowerCamelCase						,							axis=0					)
    lowerCamelCase_       	=		np.array(_lowerCamelCase					).astype(np.floataa					) / 2_55.0
    lowerCamelCase_       	=		image.transpose(0						,							3						,							1						,							2					)
    lowerCamelCase_       	=		2.0 * image - 1.0
    lowerCamelCase_       	=		torch.from_numpy(_lowerCamelCase					)
  elif isinstance(image[0]						,							torch.Tensor					):
    lowerCamelCase_       	=		torch.cat(_lowerCamelCase						,							dim=0					)
  return image







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[List, PIL.Image.Image, torch.Tensor]					) ->							Dict:
  if isinstance(_lowerCamelCase						,							torch.Tensor					):
    return mask
  elif isinstance(_lowerCamelCase						,							PIL.Image.Image					):
    lowerCamelCase_       	=		[mask]

  if isinstance(mask[0]						,							PIL.Image.Image					):
    lowerCamelCase_	,      lowerCamelCase_       	=		mask[0].size
    lowerCamelCase_	,      lowerCamelCase_       	=		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    lowerCamelCase_       	=		[np.array(m.convert('L'					).resize((w, h)						,							resample=PIL_INTERPOLATION['nearest']					)					)[None, :] for m in mask]
    lowerCamelCase_       	=		np.concatenate(_lowerCamelCase						,							axis=0					)
    lowerCamelCase_       	=		mask.astype(np.floataa					) / 2_55.0
    lowerCamelCase_       	=		0
    lowerCamelCase_       	=		1
    lowerCamelCase_       	=		torch.from_numpy(_lowerCamelCase					)
  elif isinstance(mask[0]						,							torch.Tensor					):
    lowerCamelCase_       	=		torch.cat(_lowerCamelCase						,							dim=0					)
  return mask






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	UNetaDModel
    SCREAMING_SNAKE_CASE		:	RePaintScheduler






    def __init__(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]   )	->						Dict:
      super().__init__()
      self.register_modules(unet=__SCREAMING_SNAKE_CASE			,		scheduler=__SCREAMING_SNAKE_CASE   )






    @torch.no_grad()
    def __call__(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Union[torch.Tensor, PIL.Image.Image]			,		__SCREAMING_SNAKE_CASE	:				Union[torch.Tensor, PIL.Image.Image]			,		__SCREAMING_SNAKE_CASE	:				int = 250			,		__SCREAMING_SNAKE_CASE	:				float = 0.0			,		__SCREAMING_SNAKE_CASE	:				int = 10			,		__SCREAMING_SNAKE_CASE	:				int = 10			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "pil"			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		)	->						Union[ImagePipelineOutput, Tuple]:
      lowerCamelCase_       	=		image

      lowerCamelCase_       	=		_preprocess_image(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		original_image.to(device=self.device			,		dtype=self.unet.dtype   )
      lowerCamelCase_       	=		_preprocess_mask(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		mask_image.to(device=self.device			,		dtype=self.unet.dtype   )

      lowerCamelCase_       	=		original_image.shape[0]

      # sample gaussian noise to begin the loop
      if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != batch_size:
        raise ValueError(
            F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE   )}, but requested an effective batch'''
            F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

      lowerCamelCase_       	=		original_image.shape
      lowerCamelCase_       	=		randn_tensor(__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		device=self.device			,		dtype=self.unet.dtype   )

      # set step values
      self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		self.device   )
      lowerCamelCase_       	=		eta

      lowerCamelCase_       	=		self.scheduler.timesteps[0] + 1
      lowerCamelCase_       	=		generator[0] if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) else generator
      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
        if t < t_last:
          # predict the noise residual
          lowerCamelCase_       	=		self.unet(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ).sample
          # compute previous image: x_t -> x_t-1
          lowerCamelCase_       	=		self.scheduler.step(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ).prev_sample

        else:
          # compute the reverse: x_t-1 -> x_t
          lowerCamelCase_       	=		self.scheduler.undo_step(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		t

      lowerCamelCase_       	=		(image / 2 + 0.5).clamp(0			,		1   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   ).numpy()
      if output_type == "pil":
        lowerCamelCase_       	=		self.numpy_to_pil(__SCREAMING_SNAKE_CASE   )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[str]          =		{}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							str          =		['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Any          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




